A centralized monitoring interface for microgrid using lab view

Abstract Micro-grids care the electric utility main grid through allowing the integration of emergent distributions of different energy resources. In addition, distributed energy resources (DERs) decrease energy losses and improving the efficiency of the electric power system. The DERs integrated micro grid structure will involuntarily require perfect, specific, as well as reactive power control to make sure well-organized power flow. It is important to cultivate a back-end system (user interface) to permit simple access to the whole power system development. This paper proposes a Lab VIEW based user-friendly front-end and back-end interfaces for PV system integration in micro grid. The proposed interface permits a control programming in the block end which offers the opportunities for deploying the comprehensive and inclusive control strategies. The system consists of load scheduling and PV generation during load dispatch epoch. The proposed LabVIEW control system also manage to pay for monitoring and tracking of maximum PV power point selections which allow insightful PV analysis forecast to establish the subsequent future exploit. The proposed PV interface used artificial neural network back propagation for the PV power Load Forecasting.

[1]  S. Umashankar,et al.  Real time simulation of solar photovoltaic module using labview data acquisition card , 2013, 2013 International Conference on Energy Efficient Technologies for Sustainability.

[2]  Pierluigi Siano,et al.  Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems , 2017 .

[3]  S. Jeevananthan,et al.  FPGA based practical implementation of NPC-MLI with SVPWM for an autonomous operation PV system with capacitor balancing , 2014 .

[4]  T. Oshiro,et al.  Analysis of MPPT characteristics in photovoltaic power system , 1997 .

[5]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[6]  G. T. Heinemann,et al.  The Relationship Between Summer Weather and Summer Loads - A Regression Analysis , 1966 .

[7]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[8]  Zbigniew Leonowicz,et al.  Rescheduling of Generators with Pumped Hydro Storage Units to Relieve Congestion Incorporating Flower Pollination Optimization , 2019 .

[9]  Hirotaka Koizumi,et al.  Dynamic evaluation of maximum power point tracking operation with PV array simulator , 2003 .

[10]  Sanjeevikumar Padmanaban,et al.  Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach , 2019, Energies.

[11]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[12]  Sanjeevikumar Padmanaban,et al.  Insulation condition assessment of high‐voltage rotating machines using hybrid techniques , 2018, IET Generation, Transmission & Distribution.

[13]  S. Vemuri,et al.  Neural network based short term load forecasting , 1993 .

[14]  Tomonobu Senjyu,et al.  A neural network based several-hour-ahead electric load forecasting using similar days approach , 2006 .

[15]  Yusuff Adedayo,et al.  A Simple Multilevel Space Vector Modulation Technique and MATLAB System Generator Built FPGA Implementation for Three-Level Neutral-Point Clamped Inverter , 2019 .

[16]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[17]  S. Jeevananthan,et al.  A Timing Correction Algorithm-Based Extended SVM for Three-Level Neutral-Point-Clamped MLI in Over Modulation Zone , 2018, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[18]  Mohd Tariq,et al.  Design and Implementation of Fourth Arm for Elimination of Bearing Current in NPC-MLI-Fed Induction Motor Drive , 2018 .

[19]  M. E. Ropp,et al.  Comparative study of maximum power point tracking algorithms , 2003 .

[20]  C. Bharatiraja,et al.  A Three-Phase Transformerless T-Type- NPC-MLI for Grid Connected PV Systems with Common-Mode Leakage Current Mitigation , 2019, Energies.

[21]  C. Bharatiraja,et al.  Design and Implementation of Real Time Charging Optimization for Hybrid Electric Vehicles , 2016 .

[22]  M. Vitelli,et al.  Optimization of perturb and observe maximum power point tracking method , 2005, IEEE Transactions on Power Electronics.

[23]  C. Bharatiraja,et al.  Fault Tolerant Operation of Parallel-Connected 3L-Neutral-Point Clamped Back-to-Back Converters Serving to Large Hydro-Generating Units , 2018, IEEE Transactions on Industry Applications.

[24]  S. Jeevananthan,et al.  Vector selection approach-based hexagonal hysteresis space vector current controller for a three phase diode clamped MLI with capacitor voltage balancing , 2016 .

[25]  Mike Ropp,et al.  Comparative study of maximum power point tracking algorithms using an experimental, programmable, maximum power point tracking test bed , 2000, Conference Record of the Twenty-Eighth IEEE Photovoltaic Specialists Conference - 2000 (Cat. No.00CH37036).

[26]  Tsutomu Hoshino,et al.  Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions , 1995 .

[27]  C. Bharatiraja,et al.  A Novel Reduced Switch Single Source MLI Topology with Variable Input Overvoltage Control , 2013 .

[28]  C. Bharatiraja,et al.  Improved SVPWM vector selection approaches in OVM region to reduce common-mode voltage for three-level neutral point clamped inverter , 2016 .

[29]  Alireza Khotanzad,et al.  An artificial neural network hourly temperature forecaster with applications in load forecasting , 1996 .

[30]  Vigna K. Ramachandaramurthy,et al.  Investigations of multi-carrier pulse width modulation schemes for diode free neutral point clamped multilevel inverters , 2019 .

[31]  Padmanaban Sanjeevikumar,et al.  Investigation of Slim Type BLDC Motor Drive with Torque Ripple Minimization using Abridged Space-Vector PWM Control Method , 2017 .